LSDT: Latent Sparse Domain Transfer Learning for Visual Adaptation

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Lei ZhangDavid Zhang

Abstract

We propose a novel reconstruction-based transfer learning method called latent sparse domain transfer (LSDT) for domain adaptation and visual categorization of heterogeneous data. For handling cross-domain distribution mismatch, we advocate reconstructing the target domain data with the combined source and target domain data points based on ℓ1-norm sparse coding. Furthermore, we propose a joint learning model for simultaneous optimization of the sparse coding and the optimal subspace representation. In addition, we generalize the proposed LSDT model into a kernel-based linear/nonlinear basis transformation learning framework for tackling nonlinear subspace shifts in reproduced kernel Hilbert space. The proposed methods have three advantages: 1) the latent space and the reconstruction are jointly learned for pursuit of an optimal subspace transfer; 2) with the theory of sparse subspace clustering, a few valuable source and target data points are formulated to reconstruct the target data with noise (outliers) from source domain removed during domain adaptation, such that the robustness is guaranteed; and 3) a nonlinear projection of some latent space with kernel is easily generalized for dealing with highly nonlinear domain shift...Continue Reading

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Citations

Apr 18, 2016·Computers in Biology and Medicine·Maryam AfzaliHamid Soltanian-Zadeh
Mar 30, 2018·Sensors·Katharina DormannUwe D Hanebeck
Oct 25, 2016·IEEE Transactions on Cybernetics·Huaping LiuDi Guo
Jun 6, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Shuang LiCheng Wu
Jun 9, 2018·Interdisciplinary Sciences, Computational Life Sciences·Yun WuDong Xu

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